{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling to maximum value - MaxAbsScaling\n",
    "\n",
    "Maximum absolute scaling scales the data to its maximum value:\n",
    "\n",
    "X_scaled = X / X.max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MaxAbsScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>advice</th>\n",
       "      <th>cab</th>\n",
       "      <th>decker</th>\n",
       "      <th>etis</th>\n",
       "      <th>eurobond</th>\n",
       "      <th>halligan</th>\n",
       "      <th>keen</th>\n",
       "      <th>pvr</th>\n",
       "      <th>refundable</th>\n",
       "      <th>soda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   advice  cab  decker  etis  eurobond  halligan  keen  pvr  refundable  soda\n",
       "0     0.0  0.0     2.0   0.0       0.0       0.0   0.0  0.0         0.0   0.0\n",
       "1     0.0  0.0     2.0   0.0       0.0       0.0   0.0  0.0         0.0   0.0\n",
       "2     1.0  0.0     0.0   0.0       0.0       0.0   0.0  0.0         0.0   0.0\n",
       "3     0.0  0.0     0.0   0.0       0.0       0.0   0.0  0.0         1.0   0.0\n",
       "4     0.0  0.0     2.0   0.0       0.0       0.0   0.0  0.0         0.0   0.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the bag of words dataset\n",
    "\n",
    "data = pd.read_csv(\"bag_of_words.csv\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the scaler\n",
    "scaler = MaxAbsScaler().set_output(transform=\"pandas\")\n",
    "\n",
    "# fit the scaler to the data, it will learn the parameters\n",
    "scaler.fit(data)\n",
    "\n",
    "# transform data\n",
    "data_scaled = scaler.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.,  6.,  2.,  2., 11.,  4.,  3.,  6., 52.,  2.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the scaler stores the maximum values of the features as learned from train set\n",
    "scaler.max_abs_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>decker</th>\n",
       "      <th>etis</th>\n",
       "      <th>eurobond</th>\n",
       "      <th>halligan</th>\n",
       "      <th>keen</th>\n",
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       "      <th>soda</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "     advice  cab  decker  etis  eurobond  halligan  keen  pvr  refundable  \\\n",
       "0  0.000000  0.0     1.0   0.0       0.0       0.0   0.0  0.0    0.000000   \n",
       "1  0.000000  0.0     1.0   0.0       0.0       0.0   0.0  0.0    0.000000   \n",
       "2  0.142857  0.0     0.0   0.0       0.0       0.0   0.0  0.0    0.000000   \n",
       "3  0.000000  0.0     0.0   0.0       0.0       0.0   0.0  0.0    0.019231   \n",
       "4  0.000000  0.0     1.0   0.0       0.0       0.0   0.0  0.0    0.000000   \n",
       "\n",
       "   soda  \n",
       "0   0.0  \n",
       "1   0.0  \n",
       "2   0.0  \n",
       "3   0.0  \n",
       "4   0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>advice</th>\n",
       "      <th>cab</th>\n",
       "      <th>decker</th>\n",
       "      <th>etis</th>\n",
       "      <th>eurobond</th>\n",
       "      <th>halligan</th>\n",
       "      <th>keen</th>\n",
       "      <th>pvr</th>\n",
       "      <th>refundable</th>\n",
       "      <th>soda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.427954</td>\n",
       "      <td>0.008646</td>\n",
       "      <td>0.007925</td>\n",
       "      <td>0.007925</td>\n",
       "      <td>0.219020</td>\n",
       "      <td>0.072767</td>\n",
       "      <td>0.009366</td>\n",
       "      <td>0.177954</td>\n",
       "      <td>2.336455</td>\n",
       "      <td>0.007925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.940093</td>\n",
       "      <td>0.227675</td>\n",
       "      <td>0.110426</td>\n",
       "      <td>0.103691</td>\n",
       "      <td>0.695758</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.144294</td>\n",
       "      <td>0.581526</td>\n",
       "      <td>6.151411</td>\n",
       "      <td>0.096488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            advice          cab       decker         etis     eurobond  \\\n",
       "count  1388.000000  1388.000000  1388.000000  1388.000000  1388.000000   \n",
       "mean      0.427954     0.008646     0.007925     0.007925     0.219020   \n",
       "std       0.940093     0.227675     0.110426     0.103691     0.695758   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       7.000000     6.000000     2.000000     2.000000    11.000000   \n",
       "\n",
       "          halligan         keen          pvr   refundable         soda  \n",
       "count  1388.000000  1388.000000  1388.000000  1388.000000  1388.000000  \n",
       "mean      0.072767     0.009366     0.177954     2.336455     0.007925  \n",
       "std       0.394309     0.144294     0.581526     6.151411     0.096488  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     1.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     2.000000     0.000000  \n",
       "max       4.000000     3.000000     6.000000    52.000000     2.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the original value statistics\n",
    "\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>advice</th>\n",
       "      <th>cab</th>\n",
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       "      <th>refundable</th>\n",
       "      <th>soda</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
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       "      <td>1388.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.061136</td>\n",
       "      <td>0.001441</td>\n",
       "      <td>0.003963</td>\n",
       "      <td>0.003963</td>\n",
       "      <td>0.019911</td>\n",
       "      <td>0.018192</td>\n",
       "      <td>0.003122</td>\n",
       "      <td>0.029659</td>\n",
       "      <td>0.044932</td>\n",
       "      <td>0.003963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.134299</td>\n",
       "      <td>0.037946</td>\n",
       "      <td>0.055213</td>\n",
       "      <td>0.051846</td>\n",
       "      <td>0.063251</td>\n",
       "      <td>0.098577</td>\n",
       "      <td>0.048098</td>\n",
       "      <td>0.096921</td>\n",
       "      <td>0.118296</td>\n",
       "      <td>0.048244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            advice          cab       decker         etis     eurobond  \\\n",
       "count  1388.000000  1388.000000  1388.000000  1388.000000  1388.000000   \n",
       "mean      0.061136     0.001441     0.003963     0.003963     0.019911   \n",
       "std       0.134299     0.037946     0.055213     0.051846     0.063251   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "          halligan         keen          pvr   refundable         soda  \n",
       "count  1388.000000  1388.000000  1388.000000  1388.000000  1388.000000  \n",
       "mean      0.018192     0.003122     0.029659     0.044932     0.003963  \n",
       "std       0.098577     0.048098     0.096921     0.118296     0.048244  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.019231     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.038462     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the values after scaling\n",
    "\n",
    "data_scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.dpi\"] = 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(bins=20, figsize=(20, 20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_scaled.hist(bins=20, figsize=(20, 20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Centering + MaxAbsScaling\n",
    "\n",
    "We can center the distributions at zero and then scale to its absolute maximum, as recommended by Scikit-learn by combining the use of 2 transformers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MaxAbsScaler, StandardScaler\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556\n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842\n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260\n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945\n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the California House price data from Scikit-learn\n",
    "X, y = fetch_california_housing(return_X_y=True, as_frame=True)\n",
    "X.drop(labels=[\"Latitude\", \"Longitude\"], axis=1, inplace=True)\n",
    "\n",
    "# display top 5 rows\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14448, 6), (6192, 6))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate the data into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the StandardScaler so that it removes the mean\n",
    "# but does not divide by the standard deviation\n",
    "scaler_mean = StandardScaler(with_mean=True, with_std=False)\n",
    "\n",
    "# set up the MaxAbsScaler normally\n",
    "scaler_maxabs = MaxAbsScaler()\n",
    "\n",
    "scaler = Pipeline([\n",
    "    (\"scaler_mean\", scaler_mean),\n",
    "    (\"scaler_max\", scaler_maxabs),\n",
    "]).set_output(transform=\"pandas\")\n",
    "\n",
    "# fit the scalers to the train set, it will learn the parameters\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# transform train and test sets\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.880013</td>\n",
       "      <td>28.687984</td>\n",
       "      <td>5.442057</td>\n",
       "      <td>1.101109</td>\n",
       "      <td>1426.222061</td>\n",
       "      <td>3.140976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.920007</td>\n",
       "      <td>12.560416</td>\n",
       "      <td>2.862733</td>\n",
       "      <td>0.519956</td>\n",
       "      <td>1091.567168</td>\n",
       "      <td>15.796292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.465753</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.552150</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.414452</td>\n",
       "      <td>1.006494</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>2.436452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.529600</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.227365</td>\n",
       "      <td>1.048741</td>\n",
       "      <td>1169.500000</td>\n",
       "      <td>2.825041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.768750</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.064257</td>\n",
       "      <td>1.098434</td>\n",
       "      <td>1727.250000</td>\n",
       "      <td>3.285501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>25.636364</td>\n",
       "      <td>16305.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MedInc     HouseAge     AveRooms    AveBedrms    Population  \\\n",
       "count  6192.000000  6192.000000  6192.000000  6192.000000   6192.000000   \n",
       "mean      3.880013    28.687984     5.442057     1.101109   1426.222061   \n",
       "std       1.920007    12.560416     2.862733     0.519956   1091.567168   \n",
       "min       0.499900     1.000000     1.465753     0.500000      8.000000   \n",
       "25%       2.552150    18.000000     4.414452     1.006494    796.000000   \n",
       "50%       3.529600    29.000000     5.227365     1.048741   1169.500000   \n",
       "75%       4.768750    37.000000     6.064257     1.098434   1727.250000   \n",
       "max      15.000100    52.000000   141.909091    25.636364  16305.000000   \n",
       "\n",
       "          AveOccup  \n",
       "count  6192.000000  \n",
       "mean      3.140976  \n",
       "std      15.796292  \n",
       "min       0.692308  \n",
       "25%       2.436452  \n",
       "50%       2.825041  \n",
       "75%       3.285501  \n",
       "max    1243.333333  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the original value statistics\n",
    "X_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "      <td>6192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.001199</td>\n",
       "      <td>0.002509</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.172454</td>\n",
       "      <td>0.454779</td>\n",
       "      <td>0.022522</td>\n",
       "      <td>0.015770</td>\n",
       "      <td>0.031864</td>\n",
       "      <td>0.026474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.302402</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.031136</td>\n",
       "      <td>-0.018039</td>\n",
       "      <td>-0.041369</td>\n",
       "      <td>-0.003936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.118069</td>\n",
       "      <td>-0.384475</td>\n",
       "      <td>-0.007938</td>\n",
       "      <td>-0.002677</td>\n",
       "      <td>-0.018366</td>\n",
       "      <td>-0.001012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.030275</td>\n",
       "      <td>0.013806</td>\n",
       "      <td>-0.001542</td>\n",
       "      <td>-0.001396</td>\n",
       "      <td>-0.007463</td>\n",
       "      <td>-0.000361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.081025</td>\n",
       "      <td>0.303465</td>\n",
       "      <td>0.005042</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.008818</td>\n",
       "      <td>0.000411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846575</td>\n",
       "      <td>1.073761</td>\n",
       "      <td>0.744318</td>\n",
       "      <td>0.434361</td>\n",
       "      <td>2.078678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MedInc     HouseAge     AveRooms    AveBedrms   Population  \\\n",
       "count  6192.000000  6192.000000  6192.000000  6192.000000  6192.000000   \n",
       "mean      0.001199     0.002509     0.000147     0.000192     0.000031   \n",
       "std       0.172454     0.454779     0.022522     0.015770     0.031864   \n",
       "min      -0.302402    -1.000000    -0.031136    -0.018039    -0.041369   \n",
       "25%      -0.118069    -0.384475    -0.007938    -0.002677    -0.018366   \n",
       "50%      -0.030275     0.013806    -0.001542    -0.001396    -0.007463   \n",
       "75%       0.081025     0.303465     0.005042     0.000111     0.008818   \n",
       "max       1.000000     0.846575     1.073761     0.744318     0.434361   \n",
       "\n",
       "          AveOccup  \n",
       "count  6192.000000  \n",
       "mean      0.000168  \n",
       "std       0.026474  \n",
       "min      -0.003936  \n",
       "25%      -0.001012  \n",
       "50%      -0.000361  \n",
       "75%       0.000411  \n",
       "max       2.078678  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the values after scaling\n",
    "\n",
    "X_test_scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test.hist(bins=20, figsize=(20, 12), layout=(2, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_scaled.hist(bins=20, figsize=(20, 12), layout=(2, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsml",
   "language": "python",
   "name": "fsml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "221.467px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
